Braın Based Classıfıcatıon Wıth Image Processıng And Deep Artıfıcıal Intellıgence Methods In Matlab Software Envıronment
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PDF downloads: 13
DOI:
https://doi.org/10.5281/zenodo.14188652Keywords:
Brain Tumors, Stroke, Image Processing, Deep Learning, Pre-Diagnostic MethodsAbstract
Medical image processing is an important interdisciplinary field that involves advanced
integrated computational techniques with medical sciences to enhance the visualization, analysis, and
interpretation of medical dataset or images. It consists from the use of algorithms and tools for image
acquisition, segmentation, enhancement, reconstruction, and classification, enabling healthcare
professionals to diagnose diseases more accurately and efficiently. Common modalities include MRI, CT
scans, X-rays, and ultrasound, with applications some areas such as cancer detection, cardiovascular
analysis, and neurological assessment. Recent advances in Artificial Intelligence and Machine Learning,
especially Deep Learning, have significantly improved automated image analysis, enabling faster and
more robust identification of pathologies. The integration of artificial intelligence and big data further
holds the potential to develop the personalized medicine, clinical decision-making, treatment planning,
etc. Despite its progress, challenges remain in terms of data standardization, privacy concerns, and the
need for robust validation of models in clinical settings. Brain image-based classification using image
processing and deep learning methods in MATLAB is a crucial and popular area in medical diagnostics,
aiding in the detection and categorization of neurological conditions or disorders. This review approach
combines traditional image processing techniques such as image segmentation, feature extraction, and
enhancement with cutting-edge deep learning models to classify brain abnormalities, including tumors
and stroke for pre-diagnosing phase. However, challenges remain, including the need for large, well
labeled datasets and computational resources, as well as addressing the generalization of models across
diverse patient populations.
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